第十六周作业

100-王益-浙江/README.md

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+

100-王益-浙江/test.py

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+print("这是测试文件")

106-陈婧婧-浙江/README.md

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+## 240318 - 第一次作业 - meet with github
+* 阅读官方文档和教程等学习 git，github 基本概念和操作，
+* 练习：使用 github 交作业（需要上传自己本地的文件）
+* 初步掌握 fork/commit/pull request/merge 等的基本概念及操作

106-陈婧婧-浙江/hello.py

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+# 作者: 陈婧婧
+# 邮箱：[email protected]
+# 2024年03月11日14时10分23秒
+
+print("hello world")

121-赵之胤-武汉/readme.md

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+第一周作业

124-郭丹阳-广州/README.md

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+24.3.20 - 第一次作业
+要求： github 交作业，创建自己的文件夹并且上传自己本地的文件，并将自己的文件夹远程合并

124-郭丹阳-广州/hello.py

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+print('hello,world')

129-李彦松-北京/README.md

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+first homework from badou college by lys

129-李彦松-北京/test.py

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+import datetime
+import time
+
+def remind_me():
+    while True:
+        # 获取当前日期和时间
+        current_date = datetime.datetime.now()
+        # 检查是否是周日并且当前时间是9点
+        if current_date.weekday() == 6 and current_date.hour == 9:
+            print("现在是上午9点，今天是周日，记得上课！")
+        # 每小时检查一次
+        time.sleep(3600)
+
+
+remind_me()

145-张一鸣-长沙/README.md

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+

155-刘洪伟-重庆/README.md

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+第一次提交

156-任泽宇-北京/README.md

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+text

156-任泽宇-北京/main.py

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+import tensorflow
+
+from tensorflow import keras
+
+import torch
+
+import skimage
+
+import matplotlib
+
+import cv2
+
+
+print('PyCharm')
+

174-张培天/README.md

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+2024AI人工智能，计算机视觉方向学习作业

174-张培天/第七周作业/SIFT_1.py

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+import cv2
+import numpy as np
+
+img = cv2.imread("lenna.png")
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+sift = cv2.xfeatures2d.SIFT_create()
+keypoints, descriptor = sift.detectAndCompute(gray, None)
+
+img = cv2.drawKeypoints(image=img, outImage=img, keypoints=keypoints, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, color=(51, 163, 236))
+
+cv2.imshow("sift_keypoints", img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

174-张培天/第七周作业/密度聚类.py

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+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.cluster import DBSCAN
+
+iris = datasets.load_iris()
+X = iris.data[:,:4]
+
+dbscan = DBSCAN(eps=0.4, min_samples=9)
+dbscan.fit(X)
+label_pred = dbscan.labels_
+
+x0 = X[label_pred == 0]
+x1 = X[label_pred == 1]
+x2 = X[label_pred == 2]
+
+plt.scatter(x0[:,0],x0[:,1], c='red', marker='o', label='label0')
+plt.scatter(x1[:,0],x1[:,1], c='green', marker='*', label='label1')
+plt.scatter(x2[:,0],x2[:,1], c='blue', marker='+', label='label2')
+plt.xlabel('sepal length')
+plt.ylabel('sepal width')
+plt.legend(loc=2)
+plt.show()

174-张培天/第七周作业/层次聚类.py

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+from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
+import matplotlib.pyplot as plt
+
+X = [[1,2],[3,1], [3,2], [4,4], [1,2], [1,3]]
+Z = linkage(X, 'ward')
+f = fcluster(Z,4, 'distance')
+fig = plt.figure(figsize=(5,3))
+dn = dendrogram(Z)
+plt.show()

174-张培天/第三周作业/bilinear_interpolation.py

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+import numpy as np
+import cv2
+
+def bilinear_interpolation(img,dstSize):
+    src_h, src_w, channel = img.shape
+    dst_h, dst_w = dstSize
+    if src_h == dst_h and src_w == dst_w:
+        return img.copy()
+    dst_img = np.zeros([dst_h, dst_w, channel],dtype=np.uint8)
+    x_scale, y_scale = float(src_w / dst_w), float(src_h / dst_h)
+    for c in range(channel):
+        for h in range(dst_h):
+            for w in range(dst_w):
+                x = (w + 0.5) * x_scale - 0.5
+                y = (h + 0.5) * y_scale - 0.5
+
+                x0 = int(np.floor(x))
+                x1 = min(x0 + 1, src_w - 1)
+                y0 = int(np.floor(y))
+                y1 = min(y0 + 1, src_h - 1)
+
+                temp_0 = (x1 - x) * img[y0, x0, c] + (x - x0) * img[y1, x0, c]
+                temp_1 = (x1 - x) * img[y1, x0, c] + (x - x0) * img[y1, x1, c]
+                dst_img[h, w, c] = int((y1 - y) * temp_0  + (y - y0) * temp_1)
+    return dst_img
+
+
+if __name__ == "__main__":
+    img = cv2.imread('lenna.png')
+    dstSize = (800,800)
+    dst_img = bilinear_interpolation(img,dstSize)
+    cv2.imshow("bilinear interpolation",dst_img)
+    cv2.waitKey(0)

174-张培天/第三周作业/hIstogram_equaliation.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+def gray_equalhist(file):
+    img = cv2.imread(file, 0)
+    # 灰度图像的直方图
+    hist = cv2.calcHist([img],[0],None,[256],[0,256])
+    plt.figure()
+    plt.title("Grayscale Histogram")
+    plt.xlabel("Bins")
+    plt.ylabel("# of Pixels")
+    plt.plot(hist)
+    plt.xlim([0,256])
+    plt.show()
+    # 灰度图像直方图均衡化
+    dst = cv2.equalizeHist(img)
+    plt.figure()
+    plt.hist(dst.ravel(), 256)
+    plt.show()
+
+    cv2.imshow("Histogram Equalization", np.hstack([img, dst]))
+    cv2.waitKey(0)
+
+def color_equalhist(file):
+    img = cv2.imread(file, 1)
+    cv2.imshow("src", img)
+    b, g, r = cv2.split(img)
+    bH = cv2.equalizeHist(b)
+    gH = cv2.equalizeHist(g)
+    rH = cv2.equalizeHist(r)
+
+    result = cv2.merge((bH, gH, rH))
+    cv2.imshow("dst", result)
+    cv2.waitKey(0)
+
+if __name__ == "__main__":
+    file = "lenna.png"
+    gray_equalhist(file)
+    color_equalhist(file)

174-张培天/第三周作业/sobel.py

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+import cv2 
+
+def get_border(img):
+    x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
+    y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
+
+    absX = cv2.convertScaleAbs(x)
+    absY = cv2.convertScaleAbs(y)
+
+    dst = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+    cv2.imshow("absX", absX)
+    cv2.imshow("absY", absY)
+    cv2.imshow("result", dst)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    img = cv2.imread("lenna.png", 0)
+    get_border(img)

174-张培天/第九周作业/normalization.py

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+import numpy as np
+
+def Normaization0(X):
+    return (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
+
+def Normaization1(X):
+    return (X - X.mean(axis=0)) / (X.max(axis=0) - X.min(axis=0))
+
+def Z_score(X):
+    return (X - X.mean(axis=0)) / np.std(X)
+

174-张培天/第二周作业/image_gray_binary.py

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+from skimage.color import rgb2gray
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+# 手工灰度化
+img = cv2.imread("lenna.png")
+h,w = img.shape[:2]
+img_gray_01 = np.zeros([h,w],dtype=img.dtype)
+for i in range(h):
+    for j in range(w):
+        m = img[i,j]
+        img_gray_01[i,j] = int(m[0]*0.11 + m[1]*0.59 +m[2]*0.3)
+# cv2.imshow("image show gray 01",img_gray_01)
+
+
+# 调接口灰度化
+img_gray_02 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+# cv2.imshow("image show gray 02",img_gray_02)
+
+# 手工二值化
+img_gray_binary_01 = rgb2gray(img)
+row,col= img_gray_binary_01.shape
+for i in range(row):
+    for j in range(col):
+        if img_gray_binary_01[i,j] >= 0.5:
+            img_gray_binary_01[i,j] = 1
+        else:
+            img_gray_binary_01[i,j] = 0
+# cv2.imshow("image show binary 01",img_gray_binary_01)
+# cv2.waitKey(0)
+# 调接口二值化
+img_gray = rgb2gray(img)
+img_gray_binary_02 = np.where(img_gray >= 0.5, 1, 0)
+
+plt.rcParams['font.sans-serif']=['SimHei']
+plt.rcParams['axes.unicode_minus'] =False
+plt.subplot(321)
+plt.title("原图")
+orgimg = plt.imread("lenna.png") 
+plt.imshow(orgimg)
+plt.subplot(323)
+plt.title("手工灰度图")
+plt.imshow(img_gray_01, cmap='gray')
+plt.subplot(324)
+plt.title("调接口灰度图")
+plt.imshow(img_gray_02, cmap='gray') 
+plt.subplot(325)
+plt.title("手工二值图")
+plt.imshow(img_gray_binary_01, cmap='gray')
+plt.subplot(326)
+plt.title("调接口二值图")
+plt.imshow(img_gray_binary_02, cmap='gray')
+plt.show()

174-张培天/第二周作业/nearest_interpolation.py

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+import cv2
+import numpy as np
+
+def nearInter(img,row,col):
+    h,w,t = img.shape
+    tmpImage = np.zeros([row,col,t],dtype=img.dtype)
+    rh = row / h
+    rw = col / w
+    for r in range(row):
+        for c in range(col):
+            i = int( r / rh + 0.5)
+            j = int( c / rw + 0.5)
+            tmpImage[r,c] = img[i,j]
+    return tmpImage
+
+img = cv2.imread("lenna.png")
+row = 800
+col = 800
+interpolationImg = nearInter(img,row,col)
+cv2.imshow("near interp",interpolationImg)
+cv2.imshow("imgage",img)
+cv2.waitKey(0)